http://journals.lww.com/epidem/
en-usSun, 02 Aug 2015 18:17:14 -0500Wolters Kluwer Health RSS Generatorhttp://images.journals.lww.com/epidem/XLargeThumb.00001648-201507000-00000.CV.jpeghttp://journals.lww.com/epidem/
http://journals.lww.com/epidem/Fulltext/2015/07000/Seeking_Persuasively_Null_Results.1.aspx
No abstract available]]>Thu, 04 Jun 2015 18:51:27 GMT-05:0000001648-201507000-00001http://journals.lww.com/epidem/Fulltext/2015/07000/A_General,_Multivariate_Definition_of_Causal.6.aspx
Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.]]>Thu, 04 Jun 2015 18:52:53 GMT-05:0000001648-201507000-00006http://journals.lww.com/epidem/Fulltext/2015/07000/Commentary___Generalized_Causal_Measure__The.7.aspx
No abstract available]]>Thu, 04 Jun 2015 18:54:43 GMT-05:0000001648-201507000-00007http://journals.lww.com/epidem/Fulltext/2015/07000/Rejoinder.8.aspx
No abstract available]]>Thu, 04 Jun 2015 18:56:04 GMT-05:0000001648-201507000-00008http://journals.lww.com/epidem/Fulltext/2015/07000/Toward_a_Clearer_Portrayal_of_Confounding_Bias_in.9.aspx
Recommendations for reporting instrumental variable analyses often include presenting the balance of covariates across levels of the proposed instrument and levels of the treatment. However, such presentation can be misleading as relatively small imbalances among covariates across levels of the instrument can result in greater bias because of bias amplification. We introduce bias plots and bias component plots as alternative tools for understanding biases in instrumental variable analyses. Using previously published data on proposed preference-based, geography-based, and distance-based instruments, we demonstrate why presenting covariate balance alone can be problematic, and how bias component plots can provide more accurate context for bias from omitting a covariate from an instrumental variable versus non-instrumental variable analysis. These plots can also provide relevant comparisons of different proposed instruments considered in the same data. Adaptable code is provided for creating the plots.]]>Thu, 04 Jun 2015 18:58:37 GMT-05:0000001648-201507000-00009http://journals.lww.com/epidem/Fulltext/2015/07000/Commentary___An_Even_Clearer_Portrait_of_Bias_in.10.aspx
No abstract available]]>Thu, 04 Jun 2015 18:59:52 GMT-05:0000001648-201507000-00010http://journals.lww.com/epidem/Fulltext/2015/07000/Modification_of_Traffic_related_Respiratory.15.aspx
Background: Effects of traffic-related exposures on respiratory health are well documented, but little information is available about whether asthma control influences individual susceptibility. We analyzed data from the Atlanta Commuter Exposure study to evaluate modification of associations between rush-hour commuting, in- vehicle air pollution, and selected respiratory health outcomes by asthma control status.
Methods: Between 2009 and 2011, 39 adults participated in Atlanta Commuter Exposure, and each conducted two scripted rush-hour highway commutes. In-vehicle particulate components were measured during all commutes. Among adults with asthma, we evaluated asthma control by questionnaire and spirometry. Exhaled nitric oxide, forced expiratory volume in 1 second (FEV1), and other metrics of respiratory health were measured precommute and 0, 1, 2, and 3 hours postcommute. We used mixed effects linear regression to evaluate associations between commute-related exposures and postcommute changes in metrics of respiratory health by level of asthma control.
Results: We observed increased exhaled nitric oxide across all levels of asthma control compared with precommute measurements, with largest postcommute increases observed among participants with below-median asthma control (2 hours postcommute: 14.6% [95% confidence interval {CI} = 5.7, 24.2]; 3 hours postcommute: 19.5% [95% CI = 7.8, 32.5]). No associations between in-vehicle pollutants and percent of predicted FEV1 were observed, although higher PM2.5 was associated with lower FEV1 % predicted among participants with below-median asthma control (3 hours postcommute: –7.2 [95% CI = –11.8, –2.7]).
Conclusions: Level of asthma control may influence respiratory response to in-vehicle exposures experienced during rush-hour commuting.]]>Thu, 04 Jun 2015 19:01:14 GMT-05:0000001648-201507000-00015http://journals.lww.com/epidem/Fulltext/2015/07000/Chemical_Composition_of_Fine_Particulate_Matter.16.aspx
Background: In a previous study, we provided evidence that a decline in fine particulate matter (PM2.5) air pollution during the period between 2000 and 2007 was associated with increased life expectancy in 545 counties in the United States. In this article, we investigated which chemical constituents of PM2.5 were the main drivers of the observed association.
Methods: We estimated associations between temporal changes in seven major components of PM2.5 (ammonium, sulfate, nitrate, elemental carbon matter, organic carbon matter, sodium, and silicon) and temporal changes in life expectancy in 95 counties between 2002 and 2007. We included US counties that had adequate chemical components of PM2.5 mass data across all seasons. We fitted single pollutant and multiple pollutant linear models, controlling for available socioeconomic, demographic, and smoking variables and stratifying by urban and nonurban counties.
Results: In multiple pollutant models, we found that: (1) a reduction in sulfate was associated with an increase in life expectancy; and (2) reductions in ammonium and sodium ion were associated with increases in life expectancy in nonurban counties only.
Conclusions: Our findings suggest that recent reductions in long-term exposure to sulfate, ammonium, and sodium ion between 2002 and 2007 are associated with improved public health.]]>Thu, 04 Jun 2015 19:02:25 GMT-05:0000001648-201507000-00016http://journals.lww.com/epidem/Fulltext/2015/07000/Obesity_and_Risk_of_Infection__Results_from_the.19.aspx
Background: It is well known that obesity complicates the course of several diseases. However, it is unknown whether obesity affects the risk of infection among healthy individuals.
Methods: We included 37,808 healthy participants from the Danish Blood Donor Study, who completed a questionnaire on health-related items. Obesity was defined as a body mass index ≥ 30 kg/m2. Infections among participants were identified by relevant ICD-10 codes in the Danish National Patient Register and Anatomical Therapeutic Chemical (ATC) codes in the Danish Prescription Register. Multivariable Cox proportional hazards analysis with age as the underlying timescale was used as the statistical model.
Results: During 113,717 person-years of observation, 1,233 participants were treated for infection at a hospital. Similarly, during 58,411 person-years of observation, 15,856 participants filled at least one prescription of antimicrobials. Obesity was associated with risk of hospital-based treatment for infection (women: hazard ratio [HR] = 1.5, 95% confidence interval [CI] = 1.1, 1.9; men: HR = 1.5, 95% CI = 1.2, 1.9). For specific infections, obesity was associated with increased risk of abscesses (both sexes), infections of the skin and subcutaneous tissue (men), and respiratory tract infections and cystitis (women). Similarly, obesity was associated with filled prescriptions of antimicrobials overall (women: HR = 1.22, 95% CI = 1.14, 1.30; men: HR = 1.23, 95% CI: 1.15, 1.33) and particularly with phenoxymethylpenicillin, macrolides, dicloxacillin and flucloxacillin, and broad-spectrum penicillins.
Conclusions: In a large cohort of healthy individuals, obesity was associated with risk of infection. This result warrants further studies of metabolism and the immune response.]]>Thu, 04 Jun 2015 19:04:08 GMT-05:0000001648-201507000-00019http://journals.lww.com/epidem/Fulltext/2015/07000/Using_Twitter_to_Survey_Alcohol_Use_in_the_San.27.aspx
No abstract available]]>Thu, 04 Jun 2015 19:05:21 GMT-05:0000001648-201507000-00027